Senior Managers, Data Science - job 1 of 3

As a Senior Manager, Data Science at American Express, you will focus on developing predictive modeling, improving forecasts through machine learning techniques, and generating strategic insights across finance and risk teams. Your role will involve leveraging visualization and reporting tools to share findings effectively with various stakeholders.

To qualify for the Senior Manager, Data Science position at American Express, candidates must hold a Master’s degree in a relevant field and have a minimum of one year of experience applying statistical and econometric modeling techniques. Familiarity with machine learning, SQL, and experience in presenting complex statistical concepts to non-technical audiences is also essential.

Candidates for the Senior Manager, Data Science position at American Express should be proficient in Python, R, and SQL. Experience with tools such as AWS, PySpark, machine learning techniques, and data visualization tools is also crucial to succeed in this role.

Yes, the Senior Manager, Data Science role at American Express offers the flexibility to telecommute for up to two days a week, allowing for a better work-life balance while contributing to innovative projects.

As a Senior Manager, Data Science, you can expect a competitive salary, bonus incentives, a generous retirement savings plan with a company match, comprehensive health benefits, significant parental leave, and access to career development resources, among other generous perks designed to support your well-being.

When discussing a challenging project, highlight the problem statement, your approach to data collection and modeling, the techniques used, and the insights generated. Emphasize collaboration with stakeholders and the impact of your findings on business decisions.

Explain your process for model selection, including evaluating the nature of the data, required accuracy, and interpretability. Mention metrics like precision, recall, and F1 score that you consider in your model evaluation criteria.

Discuss specific examples of how you've used SQL to extract and manipulate data for analysis. Highlight complex queries you've crafted and how they served to address specific business questions or insights in your previous roles.

Talk about how you've simplified complex statistical concepts and customized presentations for different audiences. Share examples where your communication skills contributed to informed decision-making based on your analysis.

List common techniques like regression analysis, A/B testing, or clustering, and provide real-world examples of when you've utilized these methods. Explain how they added value to your projects or insights.

Discuss strategies for managing missing data, such as imputation, deletion, or using algorithms that can handle missing values. Provide an example illustrating how you navigated a similar situation in a project.

Define overfitting in your own words, describe how it affects model performance, and mention techniques like cross-validation, regularization, or pruning that you use to mitigate it.

Share your thoughts on technologies like deep learning, cloud computing, or automation tools. Discuss how you've seen these transformations internally or their potential future impacts on data science practices.

Elaborate on how you stay current with industry trends, mentioning books, online courses, or professional networks you leverage. Highlight a recent skill you've acquired and how you intend to implement it in your work.

Express your enthusiasm for American Express’s commitment to innovation and data-driven decision-making. Discuss how your skills and values align with their business goals and how you see yourself contributing to the team.